A Survey of Data Mining Methods for Automated Diagnosis of Cardiac Autonomic Neuropathy Progression

Herbert Jelinek, Andrei Kelarev

Research output: Contribution to journalArticlepeer-review

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Abstract

Cardiac autonomic neuropathy (CAN) is a disease that occurs as a result of nerve damage causing an abnormal control of heart rate. CAN is often associated with diabetes and is important, as it can lead to an increased morbidity and mortality of the patients. The detection and management of CAN is important since early intervention can prevent further complications that may lead to sudden death from myocardial infarction or rhythm disturbance. This paper is devoted to a review of work on developing data mining techniques for automated detection of CAN. A number of different categorizations of the CAN progression have been considered in the literature, which could make it more difficult to compare the results obtained in various papers. This is the first review proposing a comprehensive survey of all categorizations of the CAN progression considered in the literature, and grouping the results obtained according to the categorization being dealt with. This novel, thorough and systematic overview of all categorizations of CAN progression will facilitate comparison of previous results and will help to guide future work.
Original languageEnglish
Pages (from-to)217-233
Number of pages17
JournalAIMS Medical Science
Volume3
Issue number2
DOIs
Publication statusPublished - 12 Jul 2016

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